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Your competition won’t know what hit them

Deep Neural Networks

Reinforcement Leaning

Enterprise AI

Deep Neural Networks

Reinforcement Leaning

Enterprise AI

AI

Deep Reinforcement Learning

In the past 3 years there has been a Cambrian Explosion of AI. Unlike machine learning approaches of the recent past, this generation is not data-centric. Instead of training with immense labeled datasets, a reinforcement learner trains in rich responsive environments where actions have consequences. Using end-state rewards, a learner acquires superhuman performance and abilities to overcome new situations. Rather than train an AI to mirror a known solution, this generation of AI learns to master a complete environment.

OLD RULES NO LONGER APPLY
A new wave of competitive offset strategy is coming – and everything will change.

AI

Deep Reinforcement Learning

In the past 3 years there has been a Cambrian Explosion of AI. Unlike machine learning approaches of the recent past, this generation is not data-centric. Instead of training with immense labeled datasets, a reinforcement learner trains in rich responsive environments where actions have consequences. Using end-state rewards, a learner acquires superhuman performance and abilities to overcome new situations. Rather than train an AI to mirror a known solution, this generation of AI learns to master a complete environment.

OLD RULES NO LONGER APPLY
A new wave of competitive offset strategy is coming – and everything will change.

Creating Positive Outcomes

FOR THE ENTERPRISE

Creating Positive Outcomes

FOR THE ENTERPRISE

AGRICULTURE

SECURITY

DEFENSE

HEALTHCARE

SUPPLY-CHAIN

ATHLETICS

FINANCE

MARKETING

MANUFACTURING

Reinforcement Learning

Not an extension of Data Analytics

Reinforcement Learning has little in common with conventional machine learning

Rather than strengthen human decision-making with analytical insights, the new paradigm strives to be fully autonomous in complex environments. AI can perform better than an expert without human limitations. Where conventional machine learning reproduces desired behaviors, deep reinforcement learning autodidactically discovers how to optimize end-state outcomes. The engineering challenge shifts to domain simulation and transfer using rich synthetic environments that realistically respond to a learner’s actions.

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An autodidactic (‘blank slate’) learner is placed in a rich environment. In this environment actions have realistic cause and effect consequences. It explores via trial and error. Gradually, the learner acquires an ability to make sense of the environment and achieve the desired end-state. When it successfully reaches an end-state, it receives a reward. This is repeated until the bot evolves.

Unlike conventional enterprise AI, the learner does not absorb labeled data. Instead, it develops end-to-end skills that are aligned to end-state rewards. In some cases learners can be trained without any historical data.

Bots train hard!

A rule of thumb is that reinforcement learning can teach a bot to perform any task a human normally learns through repetitive purposeful practice. The only complication is creating a realistic synthetic environment for training.